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Machine Learning as a Means to Adapt Requirement Changes for SDN Deployment Process in SDN Migration

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Advances in Computational Intelligence (IWANN 2019)

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Abstract

The deployment of SDN in legacy network has gained popularity across network operators as next generation network architecture. However, full deployment of SDN faces challenges in economical, organizational, and technical aspects. Hence, the deployment of SDN should be incremental over months or even years, and limited numbers of the nodes are upgraded to SDN-enabled one in each period. This forms a hybrid SDN (H-SDN) network which legacy and SDN nodes co-exist in the same network. Importantly, which and when a node should be replaced to SDN node are the common question which impacts the performance of a hybrid SDN network. Efforts to date primarily focus on determining sequence for migration which maximize the performance of traffic engineering (TE) in H-SDN network. However, most works do not take into consideration of the changes that may happen over the periods of SDN deployment. The possibility of these changes requires adaptation techniques to ensure effective migration sequence to cater present and future needs. In this article, we aim to identify the gap and propose the opportunity in which techniques originated from machine learning (ML) may play an important role in solving problem of incremental SDN deployment by alleviating the issues the occur during SDN migration as well as to improve the H-SDN deployment.

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Acknowledgment

This research work is fully supported by Telekom Malaysia (TM) R&D and Multimedia University (MMU), Cyberjaya, Malaysia. We are very thankful to the team of TM R&D for providing the support to our research studies.

Author information

Authors and Affiliations

  1. Faculty of Computing and Informatics, Multimedia University, Cyberjaya, Malaysia

    Siew Hong Wei, Tan Saw Chin & Jason Ng Binlun

  2. Faculty of Engineering, Multimedia University, Cyberjaya, Malaysia

    Lee Ching Kwang & Zulfadzli Yusoff

  3. Telekom Malaysia Research and Development, Cyberjaya, Malaysia

    Rizaluddin Kapsin

Authors
  1. Siew Hong Wei
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  2. Tan Saw Chin
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  3. Jason Ng Binlun
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  4. Lee Ching Kwang
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  5. Rizaluddin Kapsin
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  6. Zulfadzli Yusoff
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Corresponding author

Correspondence to Tan Saw Chin .

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Editors and Affiliations

  1. University of Granada, Granada, Spain

    Ignacio Rojas

  2. University of Malaga, Malaga, Spain

    Gonzalo Joya

  3. Polytechnic University of Catalonia, Barcelona, Spain

    Andreu Catala

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Wei, S.H., Chin, T.S., Binlun, J.N., Kwang, L.C., Kapsin, R., Yusoff, Z. (2019). Machine Learning as a Means to Adapt Requirement Changes for SDN Deployment Process in SDN Migration. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_52

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  • DOI: https://doi.org/10.1007/978-3-030-20518-8_52

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